Now using the optimizer: nadam
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_2 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 47, 47, 16) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_4 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 22, 22, 32) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_6 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 10, 10, 64) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_8 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 4, 4, 128) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_9 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_10 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 1, 1, 256) 0
_________________________________________________________________
dropout_5 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 256) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_6 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 5s 3ms/step - loss: 0.4718 - acc: 0.3213 - mean_squared_error: 0.4718 - val_loss: 0.0057 - val_acc: 0.6963 - val_mean_squared_error: 0.0057
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0137 - acc: 0.5543 - mean_squared_error: 0.0137 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0106 - acc: 0.6215 - mean_squared_error: 0.0106 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0091 - acc: 0.6612 - mean_squared_error: 0.0091 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0088 - acc: 0.6624 - mean_squared_error: 0.0088 - val_loss: 0.0053 - val_acc: 0.6963 - val_mean_squared_error: 0.0053
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0083 - acc: 0.6741 - mean_squared_error: 0.0083 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0082 - acc: 0.6857 - mean_squared_error: 0.0082 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0081 - acc: 0.6974 - mean_squared_error: 0.0081 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0078 - acc: 0.6904 - mean_squared_error: 0.0078 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0077 - acc: 0.7044 - mean_squared_error: 0.0077 - val_loss: 0.0055 - val_acc: 0.6963 - val_mean_squared_error: 0.0055
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0075 - acc: 0.7062 - mean_squared_error: 0.0075 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0072 - acc: 0.7074 - mean_squared_error: 0.0072 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0073 - acc: 0.7027 - mean_squared_error: 0.0073 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0071 - acc: 0.6998 - mean_squared_error: 0.0071 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0068 - acc: 0.7039 - mean_squared_error: 0.0068 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0069 - acc: 0.7050 - mean_squared_error: 0.0069 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0068 - acc: 0.6968 - mean_squared_error: 0.0068 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.7056 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7027 - mean_squared_error: 0.0065 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7009 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7097 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7044 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7015 - mean_squared_error: 0.0060 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7050 - mean_squared_error: 0.0060 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.7027 - mean_squared_error: 0.0058 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.7056 - mean_squared_error: 0.0057 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.7050 - mean_squared_error: 0.0058 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.7068 - mean_squared_error: 0.0056 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.7068 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.7068 - mean_squared_error: 0.0055 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.7068 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.7068 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.7085 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7074 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7068 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7062 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7074 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7074 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7074 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7068 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7079 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7050 - mean_squared_error: 0.0045 - val_loss: 0.0041 - val_acc: 0.6963 - val_mean_squared_error: 0.0041
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7056 - mean_squared_error: 0.0044 - val_loss: 0.0040 - val_acc: 0.6963 - val_mean_squared_error: 0.0040
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7062 - mean_squared_error: 0.0043 - val_loss: 0.0039 - val_acc: 0.6963 - val_mean_squared_error: 0.0039
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7050 - mean_squared_error: 0.0043 - val_loss: 0.0039 - val_acc: 0.6963 - val_mean_squared_error: 0.0039
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7068 - mean_squared_error: 0.0042 - val_loss: 0.0039 - val_acc: 0.6963 - val_mean_squared_error: 0.0039
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0039 - acc: 0.7079 - mean_squared_error: 0.0039 - val_loss: 0.0035 - val_acc: 0.6963 - val_mean_squared_error: 0.0035
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0038 - acc: 0.7050 - mean_squared_error: 0.0038 - val_loss: 0.0032 - val_acc: 0.6963 - val_mean_squared_error: 0.0032
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0036 - acc: 0.7033 - mean_squared_error: 0.0036 - val_loss: 0.0031 - val_acc: 0.6963 - val_mean_squared_error: 0.0031
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0034 - acc: 0.6986 - mean_squared_error: 0.0034 - val_loss: 0.0030 - val_acc: 0.6963 - val_mean_squared_error: 0.0030
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0033 - acc: 0.7138 - mean_squared_error: 0.0033 - val_loss: 0.0030 - val_acc: 0.7056 - val_mean_squared_error: 0.0030
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0032 - acc: 0.6974 - mean_squared_error: 0.0032 - val_loss: 0.0025 - val_acc: 0.6986 - val_mean_squared_error: 0.0025
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0031 - acc: 0.7056 - mean_squared_error: 0.0031 - val_loss: 0.0025 - val_acc: 0.7196 - val_mean_squared_error: 0.0025
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0029 - acc: 0.7091 - mean_squared_error: 0.0029 - val_loss: 0.0026 - val_acc: 0.6963 - val_mean_squared_error: 0.0026
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0029 - acc: 0.7126 - mean_squared_error: 0.0029 - val_loss: 0.0024 - val_acc: 0.7056 - val_mean_squared_error: 0.0024
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0028 - acc: 0.7132 - mean_squared_error: 0.0028 - val_loss: 0.0024 - val_acc: 0.7009 - val_mean_squared_error: 0.0024
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0028 - acc: 0.7114 - mean_squared_error: 0.0028 - val_loss: 0.0022 - val_acc: 0.7009 - val_mean_squared_error: 0.0022
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0027 - acc: 0.7155 - mean_squared_error: 0.0027 - val_loss: 0.0022 - val_acc: 0.7009 - val_mean_squared_error: 0.0022
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0026 - acc: 0.7103 - mean_squared_error: 0.0026 - val_loss: 0.0021 - val_acc: 0.6986 - val_mean_squared_error: 0.0021
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0026 - acc: 0.7173 - mean_squared_error: 0.0026 - val_loss: 0.0021 - val_acc: 0.7009 - val_mean_squared_error: 0.0021
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0025 - acc: 0.7120 - mean_squared_error: 0.0025 - val_loss: 0.0021 - val_acc: 0.6963 - val_mean_squared_error: 0.0021
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0025 - acc: 0.7261 - mean_squared_error: 0.0025 - val_loss: 0.0022 - val_acc: 0.6986 - val_mean_squared_error: 0.0022
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0025 - acc: 0.7150 - mean_squared_error: 0.0025 - val_loss: 0.0021 - val_acc: 0.7079 - val_mean_squared_error: 0.0021
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0024 - acc: 0.7138 - mean_squared_error: 0.0024 - val_loss: 0.0019 - val_acc: 0.7150 - val_mean_squared_error: 0.0019
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0024 - acc: 0.7196 - mean_squared_error: 0.0024 - val_loss: 0.0023 - val_acc: 0.6986 - val_mean_squared_error: 0.0023
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0024 - acc: 0.7307 - mean_squared_error: 0.0024 - val_loss: 0.0020 - val_acc: 0.7103 - val_mean_squared_error: 0.0020
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0023 - acc: 0.7202 - mean_squared_error: 0.0023 - val_loss: 0.0019 - val_acc: 0.7173 - val_mean_squared_error: 0.0019
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0023 - acc: 0.7237 - mean_squared_error: 0.0023 - val_loss: 0.0018 - val_acc: 0.7126 - val_mean_squared_error: 0.0018
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0023 - acc: 0.7237 - mean_squared_error: 0.0023 - val_loss: 0.0018 - val_acc: 0.7126 - val_mean_squared_error: 0.0018
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0022 - acc: 0.7202 - mean_squared_error: 0.0022 - val_loss: 0.0018 - val_acc: 0.7079 - val_mean_squared_error: 0.0018
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0022 - acc: 0.7313 - mean_squared_error: 0.0022 - val_loss: 0.0020 - val_acc: 0.7056 - val_mean_squared_error: 0.0020
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0022 - acc: 0.7261 - mean_squared_error: 0.0022 - val_loss: 0.0017 - val_acc: 0.7103 - val_mean_squared_error: 0.0017
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7196 - mean_squared_error: 0.0021 - val_loss: 0.0018 - val_acc: 0.7056 - val_mean_squared_error: 0.0018
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7231 - mean_squared_error: 0.0021 - val_loss: 0.0017 - val_acc: 0.7150 - val_mean_squared_error: 0.0017
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7319 - mean_squared_error: 0.0021 - val_loss: 0.0017 - val_acc: 0.7150 - val_mean_squared_error: 0.0017
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7173 - mean_squared_error: 0.0021 - val_loss: 0.0017 - val_acc: 0.7126 - val_mean_squared_error: 0.0017
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7261 - mean_squared_error: 0.0021 - val_loss: 0.0016 - val_acc: 0.7313 - val_mean_squared_error: 0.0016
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7290 - mean_squared_error: 0.0020 - val_loss: 0.0016 - val_acc: 0.7243 - val_mean_squared_error: 0.0016
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7196 - mean_squared_error: 0.0020 - val_loss: 0.0016 - val_acc: 0.7196 - val_mean_squared_error: 0.0016
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7284 - mean_squared_error: 0.0020 - val_loss: 0.0016 - val_acc: 0.7126 - val_mean_squared_error: 0.0016
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7237 - mean_squared_error: 0.0020 - val_loss: 0.0016 - val_acc: 0.7220 - val_mean_squared_error: 0.0016
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7354 - mean_squared_error: 0.0020 - val_loss: 0.0016 - val_acc: 0.7243 - val_mean_squared_error: 0.0016
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7185 - mean_squared_error: 0.0020 - val_loss: 0.0017 - val_acc: 0.7173 - val_mean_squared_error: 0.0017
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7167 - mean_squared_error: 0.0020 - val_loss: 0.0017 - val_acc: 0.7079 - val_mean_squared_error: 0.0017
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7225 - mean_squared_error: 0.0020 - val_loss: 0.0016 - val_acc: 0.7266 - val_mean_squared_error: 0.0016
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7307 - mean_squared_error: 0.0019 - val_loss: 0.0016 - val_acc: 0.7173 - val_mean_squared_error: 0.0016
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7214 - mean_squared_error: 0.0019 - val_loss: 0.0015 - val_acc: 0.7266 - val_mean_squared_error: 0.0015
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7307 - mean_squared_error: 0.0019 - val_loss: 0.0015 - val_acc: 0.7173 - val_mean_squared_error: 0.0015
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7249 - mean_squared_error: 0.0019 - val_loss: 0.0016 - val_acc: 0.7313 - val_mean_squared_error: 0.0016
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7261 - mean_squared_error: 0.0019 - val_loss: 0.0015 - val_acc: 0.7266 - val_mean_squared_error: 0.0015
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7266 - mean_squared_error: 0.0019 - val_loss: 0.0015 - val_acc: 0.7266 - val_mean_squared_error: 0.0015
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7179 - mean_squared_error: 0.0019 - val_loss: 0.0014 - val_acc: 0.7243 - val_mean_squared_error: 0.0014
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0018 - acc: 0.7261 - mean_squared_error: 0.0018 - val_loss: 0.0016 - val_acc: 0.7360 - val_mean_squared_error: 0.0016
Training complete, saving model as: nadam.h5
Now using the optimizer: adam
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_11 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_12 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 47, 47, 16) 0
_________________________________________________________________
dropout_7 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_13 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_14 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 22, 22, 32) 0
_________________________________________________________________
dropout_8 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_15 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_16 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 10, 10, 64) 0
_________________________________________________________________
dropout_9 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_17 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_18 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 4, 4, 128) 0
_________________________________________________________________
dropout_10 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_19 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_20 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_11 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_12 (Dropout) (None, 512) 0
_________________________________________________________________
dense_4 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 0.0653 - acc: 0.2850 - mean_squared_error: 0.0653 - val_loss: 0.0050 - val_acc: 0.6963 - val_mean_squared_error: 0.0050
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0100 - acc: 0.5275 - mean_squared_error: 0.0100 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0081 - acc: 0.5789 - mean_squared_error: 0.0081 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0072 - acc: 0.6186 - mean_squared_error: 0.0072 - val_loss: 0.0050 - val_acc: 0.6963 - val_mean_squared_error: 0.0050
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6396 - mean_squared_error: 0.0066 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.6525 - mean_squared_error: 0.0063 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.6618 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.6589 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.6863 - mean_squared_error: 0.0058 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6811 - mean_squared_error: 0.0057 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.6887 - mean_squared_error: 0.0058 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6910 - mean_squared_error: 0.0057 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6910 - mean_squared_error: 0.0057 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6951 - mean_squared_error: 0.0055 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6957 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.7004 - mean_squared_error: 0.0055 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.7004 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6963 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.7009 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7050 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7021 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7027 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7091 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7039 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7044 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7068 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7027 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7044 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7068 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7068 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7062 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7074 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7056 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Training complete, saving model as: adam.h5
Now using the optimizer: adamax
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_21 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_22 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 47, 47, 16) 0
_________________________________________________________________
dropout_13 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_23 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_24 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 22, 22, 32) 0
_________________________________________________________________
dropout_14 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_25 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_26 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_15 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_27 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_28 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 4, 4, 128) 0
_________________________________________________________________
dropout_16 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_29 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_30 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_15 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_17 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_3 (Flatten) (None, 256) 0
_________________________________________________________________
dense_5 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_18 (Dropout) (None, 512) 0
_________________________________________________________________
dense_6 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 0.0271 - acc: 0.4171 - mean_squared_error: 0.0271 - val_loss: 0.0072 - val_acc: 0.6963 - val_mean_squared_error: 0.0072
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0097 - acc: 0.5526 - mean_squared_error: 0.0097 - val_loss: 0.0065 - val_acc: 0.6963 - val_mean_squared_error: 0.0065
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0083 - acc: 0.5894 - mean_squared_error: 0.0083 - val_loss: 0.0054 - val_acc: 0.6963 - val_mean_squared_error: 0.0054
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0074 - acc: 0.6209 - mean_squared_error: 0.0074 - val_loss: 0.0053 - val_acc: 0.6963 - val_mean_squared_error: 0.0053
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0071 - acc: 0.6361 - mean_squared_error: 0.0071 - val_loss: 0.0053 - val_acc: 0.6963 - val_mean_squared_error: 0.0053
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6443 - mean_squared_error: 0.0066 - val_loss: 0.0054 - val_acc: 0.6963 - val_mean_squared_error: 0.0054
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.6460 - mean_squared_error: 0.0065 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.6665 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6752 - mean_squared_error: 0.0060 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6711 - mean_squared_error: 0.0060 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.6828 - mean_squared_error: 0.0058 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0059 - acc: 0.6840 - mean_squared_error: 0.0059 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.6863 - mean_squared_error: 0.0058 - val_loss: 0.0049 - val_acc: 0.6963 - val_mean_squared_error: 0.0049
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6887 - mean_squared_error: 0.0057 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6898 - mean_squared_error: 0.0056 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6898 - mean_squared_error: 0.0056 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6939 - mean_squared_error: 0.0056 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.7009 - mean_squared_error: 0.0055 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6974 - mean_squared_error: 0.0055 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6974 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6992 - mean_squared_error: 0.0054 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.7039 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.7039 - mean_squared_error: 0.0054 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6986 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.7039 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7044 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7021 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7044 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7062 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7027 - mean_squared_error: 0.0051 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7044 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7027 - mean_squared_error: 0.0049 - val_loss: 0.0041 - val_acc: 0.6963 - val_mean_squared_error: 0.0041
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7027 - mean_squared_error: 0.0048 - val_loss: 0.0038 - val_acc: 0.6963 - val_mean_squared_error: 0.0038
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7033 - mean_squared_error: 0.0044 - val_loss: 0.0035 - val_acc: 0.6963 - val_mean_squared_error: 0.0035
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.6992 - mean_squared_error: 0.0042 - val_loss: 0.0031 - val_acc: 0.6963 - val_mean_squared_error: 0.0031
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0041 - acc: 0.7079 - mean_squared_error: 0.0041 - val_loss: 0.0030 - val_acc: 0.6963 - val_mean_squared_error: 0.0030
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0038 - acc: 0.7004 - mean_squared_error: 0.0038 - val_loss: 0.0027 - val_acc: 0.6939 - val_mean_squared_error: 0.0027
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0035 - acc: 0.7068 - mean_squared_error: 0.0035 - val_loss: 0.0027 - val_acc: 0.6963 - val_mean_squared_error: 0.0027
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0035 - acc: 0.7074 - mean_squared_error: 0.0035 - val_loss: 0.0024 - val_acc: 0.7150 - val_mean_squared_error: 0.0024
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0034 - acc: 0.7009 - mean_squared_error: 0.0034 - val_loss: 0.0024 - val_acc: 0.6986 - val_mean_squared_error: 0.0024
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0032 - acc: 0.7085 - mean_squared_error: 0.0032 - val_loss: 0.0022 - val_acc: 0.7033 - val_mean_squared_error: 0.0022
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0031 - acc: 0.7114 - mean_squared_error: 0.0031 - val_loss: 0.0021 - val_acc: 0.7126 - val_mean_squared_error: 0.0021
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0030 - acc: 0.7161 - mean_squared_error: 0.0030 - val_loss: 0.0020 - val_acc: 0.7150 - val_mean_squared_error: 0.0020
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0029 - acc: 0.7144 - mean_squared_error: 0.0029 - val_loss: 0.0020 - val_acc: 0.7407 - val_mean_squared_error: 0.0020
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0028 - acc: 0.7220 - mean_squared_error: 0.0028 - val_loss: 0.0018 - val_acc: 0.7336 - val_mean_squared_error: 0.0018
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0027 - acc: 0.7272 - mean_squared_error: 0.0027 - val_loss: 0.0020 - val_acc: 0.7266 - val_mean_squared_error: 0.0020
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0026 - acc: 0.7266 - mean_squared_error: 0.0026 - val_loss: 0.0018 - val_acc: 0.7220 - val_mean_squared_error: 0.0018
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0025 - acc: 0.7202 - mean_squared_error: 0.0025 - val_loss: 0.0017 - val_acc: 0.7173 - val_mean_squared_error: 0.0017
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0025 - acc: 0.7284 - mean_squared_error: 0.0025 - val_loss: 0.0017 - val_acc: 0.7150 - val_mean_squared_error: 0.0017
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0024 - acc: 0.7313 - mean_squared_error: 0.0024 - val_loss: 0.0017 - val_acc: 0.7243 - val_mean_squared_error: 0.0017
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0024 - acc: 0.7190 - mean_squared_error: 0.0024 - val_loss: 0.0016 - val_acc: 0.7290 - val_mean_squared_error: 0.0016
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0023 - acc: 0.7284 - mean_squared_error: 0.0023 - val_loss: 0.0015 - val_acc: 0.7266 - val_mean_squared_error: 0.0015
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0022 - acc: 0.7167 - mean_squared_error: 0.0022 - val_loss: 0.0016 - val_acc: 0.7360 - val_mean_squared_error: 0.0016
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0022 - acc: 0.7266 - mean_squared_error: 0.0022 - val_loss: 0.0016 - val_acc: 0.7383 - val_mean_squared_error: 0.0016
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7383 - mean_squared_error: 0.0021 - val_loss: 0.0015 - val_acc: 0.7407 - val_mean_squared_error: 0.0015
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7325 - mean_squared_error: 0.0021 - val_loss: 0.0014 - val_acc: 0.7173 - val_mean_squared_error: 0.0014
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0021 - acc: 0.7389 - mean_squared_error: 0.0021 - val_loss: 0.0014 - val_acc: 0.7290 - val_mean_squared_error: 0.0014
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7331 - mean_squared_error: 0.0020 - val_loss: 0.0014 - val_acc: 0.7220 - val_mean_squared_error: 0.0014
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0020 - acc: 0.7261 - mean_squared_error: 0.0020 - val_loss: 0.0014 - val_acc: 0.7407 - val_mean_squared_error: 0.0014
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7366 - mean_squared_error: 0.0019 - val_loss: 0.0014 - val_acc: 0.7407 - val_mean_squared_error: 0.0014
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7407 - mean_squared_error: 0.0019 - val_loss: 0.0013 - val_acc: 0.7407 - val_mean_squared_error: 0.0013
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0019 - acc: 0.7284 - mean_squared_error: 0.0019 - val_loss: 0.0015 - val_acc: 0.7336 - val_mean_squared_error: 0.0015
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0018 - acc: 0.7465 - mean_squared_error: 0.0018 - val_loss: 0.0012 - val_acc: 0.7547 - val_mean_squared_error: 0.0012
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0018 - acc: 0.7453 - mean_squared_error: 0.0018 - val_loss: 0.0012 - val_acc: 0.7290 - val_mean_squared_error: 0.0012
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0017 - acc: 0.7488 - mean_squared_error: 0.0017 - val_loss: 0.0013 - val_acc: 0.7336 - val_mean_squared_error: 0.0013
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0017 - acc: 0.7453 - mean_squared_error: 0.0017 - val_loss: 0.0013 - val_acc: 0.7243 - val_mean_squared_error: 0.0013
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0017 - acc: 0.7412 - mean_squared_error: 0.0017 - val_loss: 0.0013 - val_acc: 0.7336 - val_mean_squared_error: 0.0013
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0017 - acc: 0.7500 - mean_squared_error: 0.0017 - val_loss: 0.0012 - val_acc: 0.7453 - val_mean_squared_error: 0.0012
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0017 - acc: 0.7512 - mean_squared_error: 0.0017 - val_loss: 0.0013 - val_acc: 0.7523 - val_mean_squared_error: 0.0013
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0016 - acc: 0.7535 - mean_squared_error: 0.0016 - val_loss: 0.0012 - val_acc: 0.7313 - val_mean_squared_error: 0.0012
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0016 - acc: 0.7430 - mean_squared_error: 0.0016 - val_loss: 0.0012 - val_acc: 0.7430 - val_mean_squared_error: 0.0012
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0015 - acc: 0.7699 - mean_squared_error: 0.0015 - val_loss: 0.0011 - val_acc: 0.7500 - val_mean_squared_error: 0.0011
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0015 - acc: 0.7599 - mean_squared_error: 0.0015 - val_loss: 0.0011 - val_acc: 0.7477 - val_mean_squared_error: 0.0011
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0015 - acc: 0.7523 - mean_squared_error: 0.0015 - val_loss: 0.0012 - val_acc: 0.7523 - val_mean_squared_error: 0.0012
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0015 - acc: 0.7564 - mean_squared_error: 0.0015 - val_loss: 0.0011 - val_acc: 0.7453 - val_mean_squared_error: 0.0011
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0015 - acc: 0.7558 - mean_squared_error: 0.0015 - val_loss: 0.0011 - val_acc: 0.7617 - val_mean_squared_error: 0.0011
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0014 - acc: 0.7535 - mean_squared_error: 0.0014 - val_loss: 0.0011 - val_acc: 0.7570 - val_mean_squared_error: 0.0011
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0014 - acc: 0.7582 - mean_squared_error: 0.0014 - val_loss: 0.0011 - val_acc: 0.7500 - val_mean_squared_error: 0.0011
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0014 - acc: 0.7704 - mean_squared_error: 0.0014 - val_loss: 0.0012 - val_acc: 0.7617 - val_mean_squared_error: 0.0012
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0014 - acc: 0.7605 - mean_squared_error: 0.0014 - val_loss: 0.0010 - val_acc: 0.7593 - val_mean_squared_error: 0.0010
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0014 - acc: 0.7681 - mean_squared_error: 0.0014 - val_loss: 0.0011 - val_acc: 0.7687 - val_mean_squared_error: 0.0011
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0014 - acc: 0.7728 - mean_squared_error: 0.0014 - val_loss: 0.0011 - val_acc: 0.7547 - val_mean_squared_error: 0.0011
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0013 - acc: 0.7704 - mean_squared_error: 0.0013 - val_loss: 0.0010 - val_acc: 0.7850 - val_mean_squared_error: 0.0010
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0013 - acc: 0.7675 - mean_squared_error: 0.0013 - val_loss: 0.0011 - val_acc: 0.8061 - val_mean_squared_error: 0.0011
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0013 - acc: 0.7669 - mean_squared_error: 0.0013 - val_loss: 0.0011 - val_acc: 0.7874 - val_mean_squared_error: 0.0011
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0013 - acc: 0.7658 - mean_squared_error: 0.0013 - val_loss: 0.0011 - val_acc: 0.7664 - val_mean_squared_error: 0.0011
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0013 - acc: 0.7780 - mean_squared_error: 0.0013 - val_loss: 0.0010 - val_acc: 0.7734 - val_mean_squared_error: 0.0010
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7699 - mean_squared_error: 0.0012 - val_loss: 0.0011 - val_acc: 0.7897 - val_mean_squared_error: 0.0011
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0013 - acc: 0.7722 - mean_squared_error: 0.0013 - val_loss: 9.7347e-04 - val_acc: 0.7850 - val_mean_squared_error: 9.7347e-04
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7763 - mean_squared_error: 0.0012 - val_loss: 0.0010 - val_acc: 0.8061 - val_mean_squared_error: 0.0010
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7856 - mean_squared_error: 0.0012 - val_loss: 0.0011 - val_acc: 0.8014 - val_mean_squared_error: 0.0011
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7991 - mean_squared_error: 0.0012 - val_loss: 0.0011 - val_acc: 0.8014 - val_mean_squared_error: 0.0011
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7798 - mean_squared_error: 0.0012 - val_loss: 9.7197e-04 - val_acc: 0.7710 - val_mean_squared_error: 9.7197e-04
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7757 - mean_squared_error: 0.0012 - val_loss: 9.2048e-04 - val_acc: 0.8037 - val_mean_squared_error: 9.2048e-04
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7681 - mean_squared_error: 0.0012 - val_loss: 9.2315e-04 - val_acc: 0.7897 - val_mean_squared_error: 9.2315e-04
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7921 - mean_squared_error: 0.0012 - val_loss: 9.3344e-04 - val_acc: 0.7944 - val_mean_squared_error: 9.3344e-04
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0012 - acc: 0.7880 - mean_squared_error: 0.0012 - val_loss: 9.9460e-04 - val_acc: 0.8014 - val_mean_squared_error: 9.9460e-04
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0011 - acc: 0.7775 - mean_squared_error: 0.0011 - val_loss: 0.0010 - val_acc: 0.7921 - val_mean_squared_error: 0.0010
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0011 - acc: 0.7880 - mean_squared_error: 0.0011 - val_loss: 9.5054e-04 - val_acc: 0.8201 - val_mean_squared_error: 9.5054e-04
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0011 - acc: 0.7897 - mean_squared_error: 0.0011 - val_loss: 9.4231e-04 - val_acc: 0.7944 - val_mean_squared_error: 9.4231e-04
Training complete, saving model as: adamax.h5
Now using the optimizer: sdg
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_31 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_32 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_16 (MaxPooling (None, 47, 47, 16) 0
_________________________________________________________________
dropout_19 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_33 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_34 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_17 (MaxPooling (None, 22, 22, 32) 0
_________________________________________________________________
dropout_20 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_35 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_36 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_18 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_21 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_37 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_38 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 4, 4, 128) 0
_________________________________________________________________
dropout_22 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_39 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_40 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_20 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_23 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 256) 0
_________________________________________________________________
dense_7 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_24 (Dropout) (None, 512) 0
_________________________________________________________________
dense_8 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 0.0959 - acc: 0.1606 - mean_squared_error: 0.0959 - val_loss: 0.0312 - val_acc: 0.6822 - val_mean_squared_error: 0.0312
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0193 - acc: 0.4019 - mean_squared_error: 0.0193 - val_loss: 0.0116 - val_acc: 0.6963 - val_mean_squared_error: 0.0116
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0124 - acc: 0.4731 - mean_squared_error: 0.0124 - val_loss: 0.0088 - val_acc: 0.6963 - val_mean_squared_error: 0.0088
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0107 - acc: 0.5596 - mean_squared_error: 0.0107 - val_loss: 0.0075 - val_acc: 0.6963 - val_mean_squared_error: 0.0075
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0095 - acc: 0.5631 - mean_squared_error: 0.0095 - val_loss: 0.0066 - val_acc: 0.6963 - val_mean_squared_error: 0.0066
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0089 - acc: 0.5678 - mean_squared_error: 0.0089 - val_loss: 0.0060 - val_acc: 0.6963 - val_mean_squared_error: 0.0060
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0083 - acc: 0.5935 - mean_squared_error: 0.0083 - val_loss: 0.0054 - val_acc: 0.6963 - val_mean_squared_error: 0.0054
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0079 - acc: 0.6133 - mean_squared_error: 0.0079 - val_loss: 0.0053 - val_acc: 0.6963 - val_mean_squared_error: 0.0053
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0076 - acc: 0.6069 - mean_squared_error: 0.0076 - val_loss: 0.0051 - val_acc: 0.6963 - val_mean_squared_error: 0.0051
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0074 - acc: 0.6168 - mean_squared_error: 0.0074 - val_loss: 0.0050 - val_acc: 0.6963 - val_mean_squared_error: 0.0050
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0071 - acc: 0.6308 - mean_squared_error: 0.0071 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0070 - acc: 0.6402 - mean_squared_error: 0.0070 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0068 - acc: 0.6127 - mean_squared_error: 0.0068 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6443 - mean_squared_error: 0.0066 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.6349 - mean_squared_error: 0.0065 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.6554 - mean_squared_error: 0.0064 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.6554 - mean_squared_error: 0.0063 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.6565 - mean_squared_error: 0.0062 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.6589 - mean_squared_error: 0.0061 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6618 - mean_squared_error: 0.0060 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0059 - acc: 0.6741 - mean_squared_error: 0.0059 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0059 - acc: 0.6717 - mean_squared_error: 0.0059 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.6676 - mean_squared_error: 0.0058 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6636 - mean_squared_error: 0.0057 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6711 - mean_squared_error: 0.0057 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6723 - mean_squared_error: 0.0056 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6834 - mean_squared_error: 0.0056 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6852 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6875 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6822 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6805 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6828 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6916 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6893 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6881 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6916 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6945 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6939 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6951 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6922 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.6933 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.6974 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.6980 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.6992 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.6974 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7009 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7027 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7021 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.6968 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7009 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.6992 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7027 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7044 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7033 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7033 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7062 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.6998 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7062 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7044 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7079 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7050 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7027 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7062 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7050 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7068 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7079 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7062 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7050 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7056 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7050 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7079 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7062 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Training complete, saving model as: sdg.h5
Now using the optimizer: adam_amsgrad
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_41 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_42 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_21 (MaxPooling (None, 47, 47, 16) 0
_________________________________________________________________
dropout_25 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_43 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_44 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_22 (MaxPooling (None, 22, 22, 32) 0
_________________________________________________________________
dropout_26 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_45 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_46 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_23 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_27 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_47 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_48 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 4, 4, 128) 0
_________________________________________________________________
dropout_28 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_49 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_50 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_25 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_29 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_5 (Flatten) (None, 256) 0
_________________________________________________________________
dense_9 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_30 (Dropout) (None, 512) 0
_________________________________________________________________
dense_10 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 0.0478 - acc: 0.3750 - mean_squared_error: 0.0478 - val_loss: 0.0068 - val_acc: 0.6963 - val_mean_squared_error: 0.0068
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0097 - acc: 0.5485 - mean_squared_error: 0.0097 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0080 - acc: 0.5748 - mean_squared_error: 0.0080 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0071 - acc: 0.6197 - mean_squared_error: 0.0071 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0067 - acc: 0.6227 - mean_squared_error: 0.0067 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.6454 - mean_squared_error: 0.0065 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6525 - mean_squared_error: 0.0060 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0059 - acc: 0.6647 - mean_squared_error: 0.0059 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6805 - mean_squared_error: 0.0060 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6746 - mean_squared_error: 0.0057 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6817 - mean_squared_error: 0.0056 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6811 - mean_squared_error: 0.0056 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6922 - mean_squared_error: 0.0057 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6898 - mean_squared_error: 0.0055 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6922 - mean_squared_error: 0.0055 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6910 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6957 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6998 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6974 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7044 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7015 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6963 - mean_squared_error: 0.0053 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7021 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7033 - mean_squared_error: 0.0052 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7050 - mean_squared_error: 0.0052 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7027 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7044 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7056 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7068 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7062 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7074 - mean_squared_error: 0.0051 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7068 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7079 - mean_squared_error: 0.0050 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7062 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7068 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7068 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7068 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7068 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7079 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Training complete, saving model as: adam_amsgrad.h5
Now using the optimizer: rmsprop
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_51 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_52 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_26 (MaxPooling (None, 47, 47, 16) 0
_________________________________________________________________
dropout_31 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_53 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_54 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_27 (MaxPooling (None, 22, 22, 32) 0
_________________________________________________________________
dropout_32 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_55 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_56 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_28 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_33 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_57 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_58 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_29 (MaxPooling (None, 4, 4, 128) 0
_________________________________________________________________
dropout_34 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_59 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_60 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_30 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_35 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_6 (Flatten) (None, 256) 0
_________________________________________________________________
dense_11 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_36 (Dropout) (None, 512) 0
_________________________________________________________________
dense_12 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 0.1011 - acc: 0.3359 - mean_squared_error: 0.1011 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0128 - acc: 0.5315 - mean_squared_error: 0.0128 - val_loss: 0.0097 - val_acc: 0.6963 - val_mean_squared_error: 0.0097
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0081 - acc: 0.6063 - mean_squared_error: 0.0081 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6460 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6676 - mean_squared_error: 0.0060 - val_loss: 0.0050 - val_acc: 0.6963 - val_mean_squared_error: 0.0050
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6811 - mean_squared_error: 0.0056 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6904 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6939 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.7027 - mean_squared_error: 0.0053 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7009 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7068 - mean_squared_error: 0.0050 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7074 - mean_squared_error: 0.0050 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7068 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7062 - mean_squared_error: 0.0050 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7062 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7074 - mean_squared_error: 0.0049 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0049 - val_acc: 0.6963 - val_mean_squared_error: 0.0049
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Training complete, saving model as: rmsprop.h5
Now using the optimizer: adagrad
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_61 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_62 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_31 (MaxPooling (None, 47, 47, 16) 0
_________________________________________________________________
dropout_37 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_63 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_64 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_32 (MaxPooling (None, 22, 22, 32) 0
_________________________________________________________________
dropout_38 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_65 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_66 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_33 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_39 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_67 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_68 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_34 (MaxPooling (None, 4, 4, 128) 0
_________________________________________________________________
dropout_40 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_69 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_70 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_35 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_41 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_7 (Flatten) (None, 256) 0
_________________________________________________________________
dense_13 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_42 (Dropout) (None, 512) 0
_________________________________________________________________
dense_14 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 3.5326 - acc: 0.3808 - mean_squared_error: 3.5326 - val_loss: 0.0103 - val_acc: 0.6963 - val_mean_squared_error: 0.0103
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0116 - acc: 0.5654 - mean_squared_error: 0.0116 - val_loss: 0.0082 - val_acc: 0.6963 - val_mean_squared_error: 0.0082
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0093 - acc: 0.5900 - mean_squared_error: 0.0093 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0085 - acc: 0.6454 - mean_squared_error: 0.0085 - val_loss: 0.0055 - val_acc: 0.6963 - val_mean_squared_error: 0.0055
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0080 - acc: 0.6390 - mean_squared_error: 0.0080 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0078 - acc: 0.6437 - mean_squared_error: 0.0078 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0074 - acc: 0.6589 - mean_squared_error: 0.0074 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0073 - acc: 0.6618 - mean_squared_error: 0.0073 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0072 - acc: 0.6717 - mean_squared_error: 0.0072 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0072 - acc: 0.6752 - mean_squared_error: 0.0072 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0071 - acc: 0.6822 - mean_squared_error: 0.0071 - val_loss: 0.0050 - val_acc: 0.6963 - val_mean_squared_error: 0.0050
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0071 - acc: 0.6852 - mean_squared_error: 0.0071 - val_loss: 0.0052 - val_acc: 0.6963 - val_mean_squared_error: 0.0052
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0069 - acc: 0.6928 - mean_squared_error: 0.0069 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6945 - mean_squared_error: 0.0066 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0068 - acc: 0.6898 - mean_squared_error: 0.0068 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6963 - mean_squared_error: 0.0066 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6986 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0067 - acc: 0.6986 - mean_squared_error: 0.0067 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6992 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0067 - acc: 0.6957 - mean_squared_error: 0.0067 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.7033 - mean_squared_error: 0.0066 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.7009 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.7015 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.7021 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0067 - acc: 0.7015 - mean_squared_error: 0.0067 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0066 - acc: 0.6998 - mean_squared_error: 0.0066 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7027 - mean_squared_error: 0.0065 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7009 - mean_squared_error: 0.0065 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7056 - mean_squared_error: 0.0065 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.7068 - mean_squared_error: 0.0064 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7074 - mean_squared_error: 0.0065 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7050 - mean_squared_error: 0.0065 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.7044 - mean_squared_error: 0.0064 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7074 - mean_squared_error: 0.0065 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.7044 - mean_squared_error: 0.0064 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.7068 - mean_squared_error: 0.0064 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.7062 - mean_squared_error: 0.0065 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7068 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7074 - mean_squared_error: 0.0063 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7068 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7068 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7062 - mean_squared_error: 0.0063 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7050 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.7068 - mean_squared_error: 0.0064 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7068 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7074 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7068 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7062 - mean_squared_error: 0.0062 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7068 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7056 - mean_squared_error: 0.0063 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7056 - mean_squared_error: 0.0061 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7074 - mean_squared_error: 0.0063 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7079 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7068 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7074 - mean_squared_error: 0.0063 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7068 - mean_squared_error: 0.0062 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7068 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7074 - mean_squared_error: 0.0060 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7068 - mean_squared_error: 0.0061 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7074 - mean_squared_error: 0.0063 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7074 - mean_squared_error: 0.0060 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7074 - mean_squared_error: 0.0060 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.7074 - mean_squared_error: 0.0063 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7074 - mean_squared_error: 0.0060 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7068 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7074 - mean_squared_error: 0.0060 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.7074 - mean_squared_error: 0.0060 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.7074 - mean_squared_error: 0.0061 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0059 - acc: 0.7074 - mean_squared_error: 0.0059 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.7074 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Training complete, saving model as: adagrad.h5
Now using the optimizer: adadelta
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_71 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_72 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_36 (MaxPooling (None, 47, 47, 16) 0
_________________________________________________________________
dropout_43 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_73 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_74 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_37 (MaxPooling (None, 22, 22, 32) 0
_________________________________________________________________
dropout_44 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_75 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_76 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_38 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_45 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_77 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_78 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_39 (MaxPooling (None, 4, 4, 128) 0
_________________________________________________________________
dropout_46 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_79 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_80 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_40 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_47 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_8 (Flatten) (None, 256) 0
_________________________________________________________________
dense_15 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_48 (Dropout) (None, 512) 0
_________________________________________________________________
dense_16 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 0.0568 - acc: 0.4013 - mean_squared_error: 0.0568 - val_loss: 0.0122 - val_acc: 0.6963 - val_mean_squared_error: 0.0122
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0130 - acc: 0.4924 - mean_squared_error: 0.0130 - val_loss: 0.0078 - val_acc: 0.6963 - val_mean_squared_error: 0.0078
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0102 - acc: 0.5368 - mean_squared_error: 0.0102 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0086 - acc: 0.5864 - mean_squared_error: 0.0086 - val_loss: 0.0060 - val_acc: 0.6963 - val_mean_squared_error: 0.0060
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0078 - acc: 0.6256 - mean_squared_error: 0.0078 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0072 - acc: 0.6373 - mean_squared_error: 0.0072 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0068 - acc: 0.6525 - mean_squared_error: 0.0068 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.6583 - mean_squared_error: 0.0064 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.6612 - mean_squared_error: 0.0062 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6735 - mean_squared_error: 0.0060 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.6741 - mean_squared_error: 0.0058 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6811 - mean_squared_error: 0.0057 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6811 - mean_squared_error: 0.0055 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6933 - mean_squared_error: 0.0054 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6945 - mean_squared_error: 0.0053 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7015 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.7004 - mean_squared_error: 0.0052 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.7044 - mean_squared_error: 0.0051 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7021 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7039 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7062 - mean_squared_error: 0.0050 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7062 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7062 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7050 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7074 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7079 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0045 - acc: 0.7074 - mean_squared_error: 0.0045 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0044 - acc: 0.7074 - mean_squared_error: 0.0044 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0043 - acc: 0.7074 - mean_squared_error: 0.0043 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7068 - mean_squared_error: 0.0042 - val_loss: 0.0041 - val_acc: 0.6963 - val_mean_squared_error: 0.0041
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0043 - val_acc: 0.6963 - val_mean_squared_error: 0.0043
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7068 - mean_squared_error: 0.0042 - val_loss: 0.0041 - val_acc: 0.6963 - val_mean_squared_error: 0.0041
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0042 - val_acc: 0.6963 - val_mean_squared_error: 0.0042
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0042 - acc: 0.7074 - mean_squared_error: 0.0042 - val_loss: 0.0041 - val_acc: 0.6963 - val_mean_squared_error: 0.0041
Training complete, saving model as: adadelta.h5
Now using the optimizer: sgd_nesterov
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_81 (Conv2D) (None, 96, 96, 16) 160
_________________________________________________________________
conv2d_82 (Conv2D) (None, 94, 94, 16) 2320
_________________________________________________________________
max_pooling2d_41 (MaxPooling (None, 47, 47, 16) 0
_________________________________________________________________
dropout_49 (Dropout) (None, 47, 47, 16) 0
_________________________________________________________________
conv2d_83 (Conv2D) (None, 47, 47, 32) 4640
_________________________________________________________________
conv2d_84 (Conv2D) (None, 45, 45, 32) 9248
_________________________________________________________________
max_pooling2d_42 (MaxPooling (None, 22, 22, 32) 0
_________________________________________________________________
dropout_50 (Dropout) (None, 22, 22, 32) 0
_________________________________________________________________
conv2d_85 (Conv2D) (None, 22, 22, 64) 18496
_________________________________________________________________
conv2d_86 (Conv2D) (None, 20, 20, 64) 36928
_________________________________________________________________
max_pooling2d_43 (MaxPooling (None, 10, 10, 64) 0
_________________________________________________________________
dropout_51 (Dropout) (None, 10, 10, 64) 0
_________________________________________________________________
conv2d_87 (Conv2D) (None, 10, 10, 128) 73856
_________________________________________________________________
conv2d_88 (Conv2D) (None, 8, 8, 128) 147584
_________________________________________________________________
max_pooling2d_44 (MaxPooling (None, 4, 4, 128) 0
_________________________________________________________________
dropout_52 (Dropout) (None, 4, 4, 128) 0
_________________________________________________________________
conv2d_89 (Conv2D) (None, 4, 4, 256) 295168
_________________________________________________________________
conv2d_90 (Conv2D) (None, 2, 2, 256) 590080
_________________________________________________________________
max_pooling2d_45 (MaxPooling (None, 1, 1, 256) 0
_________________________________________________________________
dropout_53 (Dropout) (None, 1, 1, 256) 0
_________________________________________________________________
flatten_9 (Flatten) (None, 256) 0
_________________________________________________________________
dense_17 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_54 (Dropout) (None, 512) 0
_________________________________________________________________
dense_18 (Dense) (None, 30) 15390
=================================================================
Total params: 1,325,454
Trainable params: 1,325,454
Non-trainable params: 0
_________________________________________________________________
Train on 1712 samples, validate on 428 samples
Epoch 1/100
1712/1712 [==============================] - 4s 2ms/step - loss: 0.0737 - acc: 0.2769 - mean_squared_error: 0.0737 - val_loss: 0.0336 - val_acc: 0.6963 - val_mean_squared_error: 0.0336
Epoch 2/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0198 - acc: 0.4206 - mean_squared_error: 0.0198 - val_loss: 0.0231 - val_acc: 0.6963 - val_mean_squared_error: 0.0231
Epoch 3/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0146 - acc: 0.4720 - mean_squared_error: 0.0146 - val_loss: 0.0162 - val_acc: 0.6963 - val_mean_squared_error: 0.0162
Epoch 4/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0123 - acc: 0.5000 - mean_squared_error: 0.0123 - val_loss: 0.0122 - val_acc: 0.6963 - val_mean_squared_error: 0.0122
Epoch 5/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0110 - acc: 0.5444 - mean_squared_error: 0.0110 - val_loss: 0.0103 - val_acc: 0.6963 - val_mean_squared_error: 0.0103
Epoch 6/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0099 - acc: 0.5391 - mean_squared_error: 0.0099 - val_loss: 0.0091 - val_acc: 0.6963 - val_mean_squared_error: 0.0091
Epoch 7/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0092 - acc: 0.5666 - mean_squared_error: 0.0092 - val_loss: 0.0080 - val_acc: 0.6963 - val_mean_squared_error: 0.0080
Epoch 8/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0088 - acc: 0.5824 - mean_squared_error: 0.0088 - val_loss: 0.0074 - val_acc: 0.6963 - val_mean_squared_error: 0.0074
Epoch 9/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0083 - acc: 0.5958 - mean_squared_error: 0.0083 - val_loss: 0.0069 - val_acc: 0.6963 - val_mean_squared_error: 0.0069
Epoch 10/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0080 - acc: 0.5923 - mean_squared_error: 0.0080 - val_loss: 0.0066 - val_acc: 0.6963 - val_mean_squared_error: 0.0066
Epoch 11/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0076 - acc: 0.6157 - mean_squared_error: 0.0076 - val_loss: 0.0063 - val_acc: 0.6963 - val_mean_squared_error: 0.0063
Epoch 12/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0074 - acc: 0.6215 - mean_squared_error: 0.0074 - val_loss: 0.0059 - val_acc: 0.6963 - val_mean_squared_error: 0.0059
Epoch 13/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0072 - acc: 0.6221 - mean_squared_error: 0.0072 - val_loss: 0.0058 - val_acc: 0.6963 - val_mean_squared_error: 0.0058
Epoch 14/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0070 - acc: 0.6262 - mean_squared_error: 0.0070 - val_loss: 0.0057 - val_acc: 0.6963 - val_mean_squared_error: 0.0057
Epoch 15/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0069 - acc: 0.6402 - mean_squared_error: 0.0069 - val_loss: 0.0054 - val_acc: 0.6963 - val_mean_squared_error: 0.0054
Epoch 16/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0067 - acc: 0.6507 - mean_squared_error: 0.0067 - val_loss: 0.0053 - val_acc: 0.6963 - val_mean_squared_error: 0.0053
Epoch 17/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0065 - acc: 0.6454 - mean_squared_error: 0.0065 - val_loss: 0.0052 - val_acc: 0.6963 - val_mean_squared_error: 0.0052
Epoch 18/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0064 - acc: 0.6437 - mean_squared_error: 0.0064 - val_loss: 0.0051 - val_acc: 0.6963 - val_mean_squared_error: 0.0051
Epoch 19/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0063 - acc: 0.6525 - mean_squared_error: 0.0063 - val_loss: 0.0050 - val_acc: 0.6963 - val_mean_squared_error: 0.0050
Epoch 20/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.6484 - mean_squared_error: 0.0062 - val_loss: 0.0050 - val_acc: 0.6963 - val_mean_squared_error: 0.0050
Epoch 21/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0062 - acc: 0.6659 - mean_squared_error: 0.0062 - val_loss: 0.0049 - val_acc: 0.6963 - val_mean_squared_error: 0.0049
Epoch 22/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0061 - acc: 0.6565 - mean_squared_error: 0.0061 - val_loss: 0.0049 - val_acc: 0.6963 - val_mean_squared_error: 0.0049
Epoch 23/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0060 - acc: 0.6717 - mean_squared_error: 0.0060 - val_loss: 0.0049 - val_acc: 0.6963 - val_mean_squared_error: 0.0049
Epoch 24/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0059 - acc: 0.6560 - mean_squared_error: 0.0059 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 25/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0059 - acc: 0.6653 - mean_squared_error: 0.0059 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 26/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0058 - acc: 0.6764 - mean_squared_error: 0.0058 - val_loss: 0.0048 - val_acc: 0.6963 - val_mean_squared_error: 0.0048
Epoch 27/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6735 - mean_squared_error: 0.0057 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 28/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6822 - mean_squared_error: 0.0057 - val_loss: 0.0047 - val_acc: 0.6963 - val_mean_squared_error: 0.0047
Epoch 29/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0057 - acc: 0.6805 - mean_squared_error: 0.0057 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 30/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6893 - mean_squared_error: 0.0055 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 31/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0056 - acc: 0.6852 - mean_squared_error: 0.0056 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 32/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6898 - mean_squared_error: 0.0055 - val_loss: 0.0046 - val_acc: 0.6963 - val_mean_squared_error: 0.0046
Epoch 33/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0055 - acc: 0.6893 - mean_squared_error: 0.0055 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 34/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6881 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 35/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6933 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 36/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0054 - acc: 0.6904 - mean_squared_error: 0.0054 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 37/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6957 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 38/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6916 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 39/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0053 - acc: 0.6939 - mean_squared_error: 0.0053 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 40/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6951 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 41/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6957 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 42/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6998 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 43/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6974 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 44/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0052 - acc: 0.6998 - mean_squared_error: 0.0052 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 45/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.6974 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 46/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.6986 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 47/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0051 - acc: 0.6980 - mean_squared_error: 0.0051 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 48/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.6992 - mean_squared_error: 0.0050 - val_loss: 0.0045 - val_acc: 0.6963 - val_mean_squared_error: 0.0045
Epoch 49/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.6974 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 50/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7004 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 51/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7021 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 52/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7044 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 53/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7027 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 54/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7039 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 55/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0050 - acc: 0.7027 - mean_squared_error: 0.0050 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 56/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7015 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 57/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7033 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 58/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.6986 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 59/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7056 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 60/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7050 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 61/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0049 - acc: 0.7050 - mean_squared_error: 0.0049 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 62/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7039 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 63/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7033 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 64/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7050 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 65/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7056 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 66/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7056 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 67/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7056 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 68/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7062 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 69/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7056 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 70/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7050 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 71/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7079 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 72/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7085 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 73/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0048 - acc: 0.7068 - mean_squared_error: 0.0048 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 74/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 75/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 76/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7085 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 77/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 78/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 79/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 80/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 81/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7085 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 82/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 83/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7079 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 84/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 85/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7074 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 86/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0047 - acc: 0.7068 - mean_squared_error: 0.0047 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 87/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 88/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 89/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 90/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 91/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 92/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 93/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 94/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 95/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 96/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 97/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 98/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 99/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Epoch 100/100
1712/1712 [==============================] - 3s 2ms/step - loss: 0.0046 - acc: 0.7074 - mean_squared_error: 0.0046 - val_loss: 0.0044 - val_acc: 0.6963 - val_mean_squared_error: 0.0044
Training complete, saving model as: sgd_nesterov.h5